About
Vaiyu, वायु, is Sanskrit for wind.
Unseen, everywhere, load-bearing. That’s how we think about AI in serious organizations. The demo on stage matters less than the infrastructure underneath it. Vaiyu Solutions was founded to build that infrastructure.
Who we are
Small by design.
Founded in October 2023 and based in Miami, Florida, Vaiyu Solutions is an interdisciplinary team of five engineers and scientists: machine learning, distributed systems, data engineering, and domain specialists in medical imaging and regulated deployment.
Everyone here is senior, and there is no bench to staff you from. We work remote-first with clients worldwide, and we stay small on purpose: the people who scope your problem are the people who ship it.
How we work
Four commitments.
01
Every number has a source
Every number on this site carries a citation. Client work gets the same discipline: measured and validated before we call it done.
02
One team, all the way through
We don’t hand off at the slide deck. Architecture, data, training, deployment, monitoring: one accountable team.
03
Your team keeps the capability
Engagements end with your team stronger. Documentation, training, and handover are line items in the statement of work.
04
Regulated by default
Healthcare taught us to build for auditors, ethics boards, and six-year lifecycles. We work the same way outside it.
Leadership
Sarthak Pati
Founder & CEO
Sarthak holds a Ph.D. in Computer Science from the Technical University of Munich (summa cum laude) and has spent 15+ years operationalizing AI in clinical environments, eight of them at the University of Pennsylvania building imaging AI used across hospital networks, then as software architect at Indiana University. Along the way he has led more than $9M in NIH/NCI-funded R&D.
He serves as Vice Chair for Algorithmic Development of the MLCommons Medical Working Group, created GaNDLF, co-led CaPTk, FeTS, and MedPerf, and has taught federated learning at MICCAI, AAAI, ISBI, and RSNA. His research appears in Nature Communications and Nature Machine Intelligence and has been covered by The Wall Street Journal.
Put the team to work.
Most of this starts with a 30-minute call. If there’s something there, the usual next step is a 2–4 week discovery sprint: framing, feasibility, and a costed plan you keep either way.